NS-HGlio

K221738

Neosoma Inc. · cleared 2022-09-27 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“NS-HGlio is a non-invasive software as a medical device (SaMD) tool intended for labeling, visualization, and volumetric quantification of high-grade brain gliomas for a population that has been pathologically diagnosed to have brain tumors.”
Algorithmdeep learning methodology
source quote (p.5)
“NS-HGlio device takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images of high-grade brain glioma acquired with standard brain tumor MRI protocols and uses a deep learning methodology to semi-automatically label the different subcomponents of the high-grade glioma.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=33 patients

endpoints: DSC (Dice Similarity Coefficient) assessing the degree of overlap between device output and the reference standard; Intraclass correlation coefficient (ICC) of the device output volumes and the reference standard

standards: IEC 62304:2006/AC:2015, FDA Guidance document, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.”

Reported performance (3 observations)

diceas written: “DSC (preoperative imaging)”0.88CI 95% CI of 0.86-0.90
source quote (p.7)
“The device achieved a mean DSC of 0.88 with 95% CI of 0.86-0.90 on preoperative imaging”
diceas written: “DSC (postoperative imaging)”0.8CI 95% CI of 0.77-0.83
source quote (p.7)
“and 0.80 with 95% CI of 0.77-0.83 on postoperative imaging”
agreement_kappaas written: “ICC”0.98CI 95% CI of 0.97-0.99
source quote (p.7)
“The mean ICC was 0.98 with 95% CI of 0.97-0.99.”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
3
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K221738